Results 1  10
of
1,252
High Breakdown Point Designs
, 1995
"... A linear model is assumed where the experimental conditions are given by the experimenter so that they are without gross errors. For this situation we regard htrimmed L p estimators which generalize the least median of squares estimator and the least trimmed squares estimators. The breakdown point ..."
Abstract
 Add to MetaCart
point of these estimators depends strongly on the underlying design and breakdown point maximizing estimators can be derived within these estimators. Also breakdown point maximizing designs can be derived. But these designs are often very different from the classically optimal designs which ensure
THE BREAKDOWN POINT — EXAMPLES AND COUNTEREXAMPLES
"... • The breakdown point plays an important though at times controversial role in statistics. In situations in which it has proved most successful there is a group of transformations which act on the sample space and which give rise to an equivariance structure. For equivariant functionals, that is t ..."
Abstract
 Add to MetaCart
• The breakdown point plays an important though at times controversial role in statistics. In situations in which it has proved most successful there is a group of transformations which act on the sample space and which give rise to an equivariance structure. For equivariant functionals
REGRESSION WITH HIGH BREAKDOWN POINT
"... Abstract. The paper discusses in details various aspects of the point estimation, classic paradigm, Hampel’s program and a new paradigm, including reliability of algorithm and its implementation, the role of accompanying procedures and of heuristics. A special attention in paid to the high breakdo ..."
Abstract
 Add to MetaCart
breakdown point estimation, corresponding prejudices and misleading ideas. It reports theoretical results as well as practical consequences, describes a reliable algorithm for evaluation of the least trimmed squares and finally illustrates by the results of analysis of real data how powerful tool
Optimal Breakdown Point Maximizing Designs
 Proceedings of PROBASTAT'94. Tatra Mountains Mathematical Publications
, 1994
"... . In linear models the breakdown point of an estimator depends strongly on the underlying design. This holds in particular for high breakdown point estimators as the least median of squares estimator or least trimmed squares estimators. It could be shown that the breakdown point is maximized if the ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
. In linear models the breakdown point of an estimator depends strongly on the underlying design. This holds in particular for high breakdown point estimators as the least median of squares estimator or least trimmed squares estimators. It could be shown that the breakdown point is maximized
INTRODUCTION TO ROBUSTNESS: BREAKDOWN POINTS
, 2005
"... Let X =(X1,..., Xn) and Z =(Z1,..., Zn) be samples of real numbers. For j = 1,..., n let X = j Z mean that Xi = Zi except for at most j values of i. More specifically, for y =(y1,..., yj) let X = j,y Z mean that for some integers ir with 1 ≤ i1 <i2 <... < ij ≤ n, Zir = yr for r = 1,..., j a ..."
Abstract
 Add to MetaCart
Let X =(X1,..., Xn) and Z =(Z1,..., Zn) be samples of real numbers. For j = 1,..., n let X = j Z mean that Xi = Zi except for at most j values of i. More specifically, for y =(y1,..., yj) let X = j,y Z mean that for some integers ir with 1 ≤ i1 <i2 <... < ij ≤ n, Zir = yr for r = 1,..., j and Zi = Xi if i ̸ = ir for r = 1,..., j. The idea is that Xi are i.i.d.
High Breakdown Point Estimation in Regression
, 2008
"... In robust regression theory the estimators, which can “resist” contamination of nearly fifty percent of the data, due to the fact that they are highly important in practice, were intensively studied. In this paper we describe three methods with high breakdown point: the least trimmed squares (LTS), ..."
Abstract
 Add to MetaCart
In robust regression theory the estimators, which can “resist” contamination of nearly fifty percent of the data, due to the fact that they are highly important in practice, were intensively studied. In this paper we describe three methods with high breakdown point: the least trimmed squares (LTS
Breakdown Points of TType Regression Estimators
, 1999
"... This paper shows that dimensionindependent positive breakdown points can be attained by a class of wellde ned generalised Mestimators with redescending scores. The solution can be determined through optimisation of ttype likelihood applied to properly weighted residuals. The highest breakdown poi ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
This paper shows that dimensionindependent positive breakdown points can be attained by a class of wellde ned generalised Mestimators with redescending scores. The solution can be determined through optimisation of ttype likelihood applied to properly weighted residuals. The highest breakdown
The Masking Breakdown Point of Multivariate Outlier Identification Rules
 J. Americ. Statist. Assoc
, 1997
"... In this paper, we consider onestep outlier identification rules for multivariate data, generalizing the concept of socalled ff outlier identifiers, as presented in Davies and Gather (1993) for the case of univariate samples. We investigate, how the finitesample breakdown points of estimators used ..."
Abstract

Cited by 35 (9 self)
 Add to MetaCart
In this paper, we consider onestep outlier identification rules for multivariate data, generalizing the concept of socalled ff outlier identifiers, as presented in Davies and Gather (1993) for the case of univariate samples. We investigate, how the finitesample breakdown points of estimators
Calculating the Breakdown Point of Sparse Linear Models
"... In robust statistics, the concept of breakdown point was introduced to quantify the robustness of an estimator in a linear regression model. Computing the breakdown point is useful in tuning some robust regression estimators (e.g., the least trimmed squares estimator). Computing the breakdown point ..."
Abstract
 Add to MetaCart
In robust statistics, the concept of breakdown point was introduced to quantify the robustness of an estimator in a linear regression model. Computing the breakdown point is useful in tuning some robust regression estimators (e.g., the least trimmed squares estimator). Computing the breakdown
Results 1  10
of
1,252